This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control system...

Dynamic systems in many branches of science and industry are often perturbed by various types of environmental noise. Analysis of this class of models are very popular among researchers. In this paper, we present a method for approximating solution of fractional-order stochastic delay differential equations driven by Brownian motion. The fractional derivatives are considered in the Caputo sense...

Within the competition in today’s business environment, the design of supply chains becomes more complex than before. This paper deals with the retailer’s location problem when customers choose their vendors, and inventory costs have been considered for retailers. In a competitive location problem, price and location of facilities affect demands of customers; consequently, simultaneous optimiza...

The description of stochastic dynamical optimization models that follows is intended to exhibit some of the connections between various formulations that have appeared in the literature, and indicate some of the diiculties that must be overcome when trying to adapt solution methods that have been successfully applied to one class of problems to an apparently related but diierent class of proble...

We study dynamic signaling in a game of stochastic stakes. Each period, privately informed agent binary type chooses whether to continue receiving return that is an increasing function both her reputation and exogenous public stakes variable or irreversibly exit the game. A strong has dominant strategy continue. In unique perfect Bayesian equilibrium, weak plays mixed depends only on current hi...

We introduce Stochastic Dynamic Cutting Plane (StoDCuP), an extension of the Dual Programming (SDDP) algorithm to solve multistage stochastic convex optimization problems. At each iteration, builds lower bounding affine functions not only for cost-to-go functions, as SDDP does, but also some or all nonlinear cost and constraint functions. show almost sure convergence StoDCuP. inexact variant St...